RRM measurement processing methods and apparatuses, and terminal and network-side device

Abstract

The present application belongs to the technical field of communications. Disclosed are RRM measurement processing methods and apparatuses, and a terminal and a network-side device. An RRM measurement processing method in the embodiments of the present application comprises: a terminal performing RRM measurement prediction on the basis of an artificial intelligence (AI) unit, so as to obtain a prediction result; and when it is determined on the basis of the prediction result that a predicted event is satisfied, the terminal triggering RRM measurement reporting or a conditional handover, wherein the predicted event is an event predicted by the terminal on the basis of the AI unit.

Description

RRM measurement processing method, device, terminal and network side equipment

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to Chinese patent application No. 202410100493.X filed on January 24, 2024. The contents of the above-mentioned Chinese patent application disclosure are hereby incorporated by reference in their entirety as a part of this application. Technical Field

[0003] The present application belongs to the field of communication technology, and specifically relates to an RRM measurement processing method, apparatus, terminal, and network-side equipment. Background Art

[0004] With the development of artificial intelligence (AI) technology, AI models have been applied to communication systems, such as AI-based channel state information (CSI) prediction, radio resource management (RRM) prediction, and event prediction. Currently, after performing actual RRM measurements, terminals will report the RRM measurement results. However, in AI-assisted mobility enhancement, how to measure and report RRM prediction results based on AI or how to conditionally switch them is still unclear. Summary of the Invention

[0005] The embodiments of the present application provide an RRM measurement processing method, apparatus, terminal, and network-side equipment, which can solve the problem in related technologies of how to measure and report or conditionally switch the RRM prediction results based on AI.

[0006] In a first aspect, a method for processing RRM measurements is provided, which is performed by a terminal. The method includes:

[0007] The terminal performs RRM measurement prediction based on the AI ​​unit and obtains the prediction result;

[0008] The terminal triggers RRM measurement reporting or conditional switching when determining, according to the prediction result, that a prediction event is satisfied;

[0009] The predicted event is an event predicted by the terminal based on the AI ​​unit.

[0010] In a second aspect, an RRM measurement processing method is provided, which is performed by a network-side device. The method includes:

[0011] The network side device sends a measurement configuration for RRM measurement to the terminal, where at least one reporting configuration of the measurement configuration includes a predicted event;

[0012] The predicted event is an event predicted by the terminal based on the AI ​​unit, and the terminal is configured to trigger RRM measurement reporting or conditional switching when a prediction result obtained by performing RRM measurement prediction based on the AI ​​unit meets the predicted event.

[0013] In a third aspect, an RRM measurement processing device is provided, including:

[0014] A prediction module is used to perform RRM measurement prediction based on the AI ​​unit and obtain prediction results;

[0015] A triggering module, configured to trigger RRM measurement reporting or conditional switching when it is determined that a prediction event is met according to the prediction result;

[0016] The predicted event is an event predicted by the device based on the AI ​​unit.

[0017] In a fourth aspect, an RRM measurement processing device is provided, including:

[0018] a sending module, configured to send a measurement configuration for RRM measurement to a terminal, wherein at least one reporting configuration of the measurement configuration includes a predicted event;

[0019] The predicted event is an event predicted by the terminal based on the AI ​​unit, and the terminal is configured to trigger RRM measurement reporting or conditional switching when a prediction result obtained by performing RRM measurement prediction based on the AI ​​unit meets the predicted event.

[0020] In a fifth aspect, a terminal is provided, comprising a processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the method described in the first aspect are implemented.

[0021] In the sixth aspect, a terminal is provided, comprising a processor and a communication interface, wherein the processor is used to perform RRM measurement prediction based on an AI unit to obtain a prediction result, and trigger RRM measurement reporting or conditional switching when it is determined that a prediction event is met according to the prediction result; wherein the predicted event is an event predicted by the terminal based on the AI ​​unit.

[0022] In the seventh aspect, a network side device is provided, which includes a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the program or instructions are executed by the processor, the steps of the method described in the second aspect are implemented.

[0023] In the eighth aspect, a network side device is provided, including a processor and a communication interface, wherein the communication interface is used to send a measurement configuration for RRM measurement to a terminal, and at least one reporting configuration of the measurement configuration includes a prediction event; the prediction event is an event predicted by the terminal based on an AI unit, and the terminal is used to trigger RRM measurement reporting or conditional switching when the prediction result obtained by performing RRM measurement prediction according to the AI ​​unit meets the prediction event.

[0024] In the ninth aspect, a readable storage medium is provided, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented, or the steps of the method described in the second aspect are implemented.

[0025] In the tenth aspect, a wireless communication system is provided, comprising: a terminal and a network side device, wherein the terminal can be used to execute the steps of the method described in the first aspect, and the network side device can be used to execute the steps of the method described in the second aspect.

[0026] In the eleventh aspect, a chip is provided, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the method as described in the first aspect, or to implement the method as described in the second aspect.

[0027] In a twelfth aspect, a computer program / program product is provided, wherein the computer program / program product is stored in a storage medium, and the program / program product is executed by at least one processor to implement the steps of the method described in the first aspect or the second aspect.

[0028] In an embodiment of the present application, the terminal performs RRM measurement prediction based on the AI ​​unit and obtains a prediction result. When it is determined that the prediction event is met according to the prediction result, the terminal triggers RRM measurement reporting or conditional switching, which helps to reduce the switching delay. It also stipulates how the terminal triggers measurement reporting or conditional switching according to the prediction result of the AI-based RRM measurement prediction in AI-assisted mobility enhancement, effectively standardizes the behavior on the terminal side, and helps to improve the communication performance between the terminal and the network side device. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] FIG1 is a block diagram of a wireless communication system to which embodiments of the present application may be applied;

[0030] FIG2 is a flow chart of an RRM measurement processing method provided in an embodiment of the present application;

[0031] FIG3a is a schematic diagram of one scenario of an RRM measurement processing method provided in an embodiment of the present application;

[0032] FIG3 b is a second schematic diagram of a scenario of an RRM measurement processing method provided in an embodiment of the present application;

[0033] FIG4 is a flowchart of another RRM measurement processing method provided in an embodiment of the present application;

[0034] FIG5 is a structural diagram of an RRM measurement processing device provided in an embodiment of the present application;

[0035] FIG6 is a structural diagram of another RRM measurement processing device provided in an embodiment of the present application;

[0036] FIG7 is a structural diagram of a communication device provided in an embodiment of the present application;

[0037] FIG8 is a structural diagram of a terminal provided in an embodiment of the present application;

[0038] FIG9 is a structural diagram of a network-side device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0039] The following will be combined with the accompanying drawings in the embodiments of this application to clearly describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of this application.

[0040] The terms "first", "second", etc. in this application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable where appropriate, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same type, and do not limit the number of objects, for example, the first object can be one or more. In addition, "or" in this application represents at least one of the connected objects. For example, "A or B" covers three options, namely, Option 1: including A but not including B; Option 2: including B but not including A; Option 3: including both A and B. The character " / " generally indicates that the objects associated before and after are in an "or" relationship.

[0041] The term "indication" in this application can be either a direct indication (or explicit indication) or an indirect indication (or implicit indication). A direct indication can be understood as the sender explicitly informing the receiver of specific information, the operation to be performed, or the result of the request in the instruction sent; an indirect indication can be understood as the receiver determining the corresponding information based on the instruction sent by the sender, or making a judgment and determining the operation to be performed or the result of the request based on the judgment result.

[0042] It is worth noting that the technology described in the embodiments of the present application is not limited to the Long Term Evolution (LTE) / LTE-Advanced (LTE-A) system, but can also be used in other wireless communication systems, such as Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), Single-carrier Frequency-Division Multiple Access (SC-FDMA) or other systems. The terms "system" and "network" in the embodiments of the present application are often used interchangeably, and the technology described can be used for the systems and radio technologies mentioned above, as well as for other systems and radio technologies. The following description describes a New Radio (NR) system for illustrative purposes, and NR terminology is used in most of the following description, but these technologies can also be applied to systems other than NR systems, such as 6th generation (6G) systems. th Generation, 6G) communication system.

[0043] FIG1 is a block diagram of a wireless communication system applicable to an embodiment of the present application. The wireless communication system includes a terminal 11 and a network-side device 12. The terminal 11 may be a mobile phone, a tablet computer (Tablet Personal Computer), a laptop computer (Laptop Computer), a notebook computer, a personal digital assistant (PDA), a handheld computer, a netbook, an ultra-mobile personal computer (UMPC), a mobile internet device (MID), an augmented reality (AR), a virtual reality (VR) device, a robot, a wearable device (Wearable Device), an aircraft (Flight Vehicle), a vehicle-mounted device (VUE), a ship-mounted device, a pedestrian user equipment (PUE), a smart home (home appliances with wireless communication capabilities, such as refrigerators, televisions, washing machines, or furniture), a game console, a personal computer (PC), an ATM, or a self-service machine, or other terminal-side devices. Wearable devices include: smart watches, smart bracelets, smart headphones, smart glasses, smart jewelry (smart bracelets, smart bracelets, smart rings, smart necklaces, smart anklets, smart anklets, etc.), smart wristbands, smart clothing, etc. Among them, the vehicle-mounted device can also be called a vehicle-mounted terminal, a vehicle-mounted controller, a vehicle-mounted module, a vehicle-mounted component, a vehicle-mounted chip or a vehicle-mounted unit, etc. It should be noted that the specific type of the terminal 11 is not limited in the embodiment of the present application. The network side device 12 may include an access network device or a core network device, wherein the access network device may also be called a radio access network (Radio Access Network, RAN) device, a radio access network function or a radio access network unit. The access network device may include a base station, a wireless local area network (WLAN) access point (AP) or a wireless fidelity (WiFi) node, etc.Among them, the base station can be referred to as Node B (NB), Evolved Node B (eNB), the next generation Node B (gNB), New Radio Node B (NR Node B), access point, Relay Base Station (RBS), Serving Base Station (SBS), Base Transceiver Station (BTS), radio base station, radio transceiver, Basic Service Set (BSS), Extended Service Set (ESS), Home Node B (HNB), Home evolved Node B (home evolved Node B), Transmission Reception Point (TRP) or other appropriate terms in the relevant field. As long as the same technical effect is achieved, the base station is not limited to specific technical vocabulary. It should be noted that in the embodiment of the present application, only the base station in the NR system is used as an example for introduction, and the specific type of the base station is not limited.

[0044] In order to better understand the technical solution of the present application, the relevant concepts involved in the embodiments of the present application are explained below.

[0045] Artificial Intelligence (AI):

[0046] AI has been widely applied in various fields. Integrating AI into wireless communication networks to significantly improve technical indicators such as throughput, latency, and user capacity is a key task for future wireless communication networks. AI modules can be implemented in a variety of ways, such as neural networks, decision trees, support vector machines, and Bayesian classifiers. This application uses neural networks as an example, but does not limit the specific type of AI module.

[0047] It should be noted that the AI ​​unit / AI model (Model) described in this application may also be referred to as an AI unit, AI model, machine learning (ML) model, ML unit, AI structure, AI function, AI feature, machine learning model, neural network, neural network function, neural network function, etc., or the AI ​​unit / AI model may also refer to a processing unit that can implement specific algorithms, formulas, processing procedures, capabilities, etc. related to AI, or the AI ​​unit / AI model may be a processing method, algorithm, function, module or unit for a specific data set, or the AI ​​unit / AI model may be a processing method, algorithm, function, module or unit running on AI / ML related hardware such as GPU, NPU, TPU, ASIC, etc., and this application does not make specific restrictions on this. Optionally, the specific data set includes the input and / or output of the AI ​​unit / AI model.

[0048] In addition, the identifier of the AI ​​unit / AI model may be an AI model identifier, an AI structure identifier, an AI algorithm identifier, or an identifier of a specific data set associated with the AI ​​unit / AI model, or an identifier of a specific scenario, environment, channel feature, or device related to the AI / ML, or an identifier of a function, feature, capability, or module related to the AI / ML. This application does not specifically limit this.

[0049] AI functionality: An AI algorithm function, which may include multiple AI models.

[0050] RRM measurement reporting:

[0051] The measurement configuration mainly consists of the measurement object, reporting configuration and measurement identifier (ID).

[0052] Measurement Object: such as the frequency point to be measured;

[0053] Report Config: includes reporting criteria (periodic / event-triggered), reference signal type (such as Synchronization Signal and PBCH block (SSB) / Channel State Information Reference Signal (CSI-RS)), measurement reporting quantity (such as any combination of Reference Signal Received Power (RSRP) / Reference Signal Received Quality (RSRQ) / Signal-to-noise and interference ratio (SINR)), whether to report beam measurement results, and the maximum number of reportable beams.

[0054] Measurement ID: used to associate a measurement object with a reporting configuration. A measurement object can be associated with multiple reporting configurations, and a reporting configuration can be associated with multiple measurement objects.

[0055] The reporting configuration can include event-triggered reporting. The events defined in NR are shown in Table 1 below:

[0056] Table 1

[0057] Taking Event A3 as an example, the meanings of the parameters for the entry and exit conditions of Event A3 are as follows:

[0058] Mn: Neighboring cell measurement result, without considering any offset;

[0059] Ofn: Neighboring cell measurement object specific offset;

[0060] Ocn: neighboring cell-level specific offset;

[0061] Mp: SpCell (primary serving cell) measurement result, without considering any offset;

[0062] Ofp: SpCell measurement object specific offset;

[0063] Ocp: SpCell cell-level specific offset;

[0064] Hys: hysteresis parameter of the event;

[0065] Off: The offset parameter of the event.

[0066] It should be noted that the meanings of other parameters involved in the above Table 1 can be referred to related technologies and will not be repeated here.

[0067] If the reporting type is event-triggered, in order to avoid frequent reporting or ping-pong switching, the base station configures a trigger time (timeToTrigger) parameter for each event. If the layer 3 (L3) filtered signal quality of one or more candidate cells within the timeToTrigger time meets the entry conditions of the event, the measurement report is triggered.

[0068] For conditional handover, the network preconfigures multiple candidate cells for the UE. The UE evaluates the execution conditions of the candidate cells and switches to the corresponding cell when the conditions are met. The execution conditions may include one or two trigger conditions.

[0069] Taking the parameters in NR as an example, condReconfigId indicates the conditional reconfiguration ID; condExecutionCond is used to configure the execution conditions for the primary cell (PCell) change; and condRRCReconfig is used to configure the configuration parameters of the target master cell group (MCG). These three parameters correspond to a set of candidate cell configurations and are used for conditional reconfiguration for a candidate PCell.

[0070] It should be noted that the execution condition includes the measurement event configured in the reporting configuration associated with the MeasurementID. For conditional switching, the measurement event supports condEventA3, condEventA4, or condEventA5, and its judgment conditions are the same as those for A3, A4, and A5 above. If the L3 filtered signal quality of one or more candidate cells within timeToTrigger meets the entry condition of the event, the UE will select the cell that meets the condition as the triggering cell and select one of the triggering cells to execute conditional reconfiguration.

[0071] The relevant technology has specified how the terminal performs RRM measurements and how to trigger the reporting of RRM measurement results. However, in AI-assisted mobility enhancement, it is not yet clear how to measure and report or conditionally switch the AI-based RRM prediction results.

[0072] The following, in conjunction with the accompanying drawings, describes in detail the RRM measurement processing method, apparatus, terminal, and network-side equipment provided in the embodiments of the present application through some embodiments and their application scenarios.

[0073] Please refer to Figure 2, which is a flow chart of an RRM measurement processing method provided in an embodiment of the present application, wherein the method is applied to a terminal. As shown in Figure 2, the method includes the following steps:

[0074] Step 201: The terminal performs RRM measurement prediction based on the AI ​​unit to obtain a prediction result.

[0075] In an embodiment of the present application, the terminal performs RRM measurement prediction based on the AI ​​unit, which may mean that the terminal performs RRM measurement prediction for a certain moment or time period in the future through the AI ​​unit to obtain a prediction result, and the prediction result is the result obtained by reasoning of the AI ​​unit, rather than the actual RRM measurement result.

[0076] Optionally, the RRM measurement prediction performed by the terminal based on the AI ​​unit may be a prediction of the beam quality or cell signal quality of the source cell / neighboring cell / target cell at a future time or time period, or a prediction of the switching time of the target cell, or a prediction of the time when the entry condition or exit condition of the measurement event is met, etc.

[0077] Step 202: When the terminal determines that a prediction event is met according to the prediction result, the terminal triggers RRM measurement reporting or conditional switching;

[0078] The predicted event is an event predicted by the terminal based on the AI ​​unit. It should be noted that the predicted event can also be understood as a measurement event predicted by the terminal based on the AI ​​unit, including the events described in Table 1 above, such as the predicted A1 event, the predicted A2 event, etc.

[0079] In an embodiment of the present application, after the terminal performs RRM measurement prediction based on the AI ​​unit and obtains the prediction result, it determines whether the prediction event is satisfied based on the prediction result. For example, assuming that the terminal predicts the cell signal quality of the neighboring cell and the serving cell in a certain time period in the future based on the AI ​​unit, and obtains the cell signal quality of the neighboring cell and the serving cell in a certain time period in the future (that is, the prediction result), if the cell signal quality of the neighboring cell in a certain time period in the future is always greater than the cell signal quality of the serving cell in a certain time period in the future, then it is considered that the prediction result satisfies the prediction event. In this case, the terminal can trigger RRM measurement reporting or conditional switching. The triggering of RRM measurement reporting refers to the terminal reporting an RRM measurement report, which includes the prediction result and / or RRM measurement result; the triggering of conditional switching refers to the terminal switching to the corresponding cell when the conditions are met according to the execution conditions of the configured candidate cell.

[0080] It should be noted that satisfying a prediction event refers to satisfying the entry or exit conditions of a prediction event, such as satisfying the entry or exit conditions of a measurement event predicted by the terminal based on the AI ​​unit. For example, for Event A1, if the cell signal quality of the serving cell predicted by the AI ​​unit meets the entry conditions of a preset threshold (or referred to as satisfying the prediction event or the predicted A1 event), the prediction event is satisfied.

[0081] In an embodiment of the present application, the terminal performs RRM measurement prediction based on the AI ​​unit and obtains a prediction result. When it is determined that the prediction event is met according to the prediction result, the terminal triggers RRM measurement reporting or conditional switching, which helps to reduce the switching delay. It also stipulates how the terminal triggers measurement reporting or conditional switching according to the prediction result of the AI-based RRM measurement prediction in AI-assisted mobility enhancement, effectively standardizes the behavior on the terminal side, and helps to improve the communication performance between the terminal and the network side device.

[0082] Optionally, the terminal determines, according to the prediction result, that the prediction event is satisfied, including any one of the following:

[0083] If the prediction result continuously satisfies the prediction event within the first time, the terminal determines that the prediction event is satisfied;

[0084] In a case where the measurement result of the RRM measurement continuously satisfies the measurement event within the second time, and the prediction result continuously satisfies the prediction event within the third time, the terminal determines that the prediction event is satisfied.

[0085] For example, in one implementation, the terminal determines whether a predicted event is satisfied based on a prediction result, which means that the terminal determines whether the predicted event is satisfied based on whether the prediction result continuously satisfies (entry conditions or exit conditions of) the predicted event within a first time period. If the prediction result continuously satisfies the predicted event within the first time period, then the predicted event is determined to be satisfied, for example, the predicted event A1 is satisfied.

[0086] Specifically, analogy with existing measurement events is that the existing measurement events determine whether the measurement event is satisfied based on the measurement results of the serving cell and / or the neighboring cell RRM measurement, while the prediction events in this application determine whether the prediction event is satisfied based on the prediction results of the serving cell and / or the neighboring cell. For example, for Event A3, if the neighboring cell measurement result is always higher than the serving cell measurement result by a preset threshold within the T1 event, then the A3 measurement event is satisfied; for Event A3 (prediction event) predicted by the terminal based on the AI ​​unit, if the prediction result of the neighboring cell at the same time is always higher than the prediction result of the serving cell by a preset threshold within the T1 time, then the A3 prediction event is satisfied. As shown in Figure 3a, the A3 measurement event meets the entry condition for the first time at time t0, and always meets the entry condition within the T1 time, then the terminal triggers measurement reporting at time t0+T1; the A3 prediction event always meets the entry condition from t0 to t0+T1 according to the prediction result at time t0 or before time t0, so if the prediction event is met at t0 or before time t0, then the terminal triggers measurement reporting at time t0.

[0087] Alternatively, in another implementation, the terminal determines whether the predicted event is met based on whether the measurement results of the RRM measurement continue to meet the measurement event within the second time, and whether the prediction results of the RRM measurement prediction based on the AI ​​unit continue to meet the predicted event within the third time.

[0088] Exemplarily, the terminal evaluates whether the measurement event is continuously satisfied based on the measurement result of the RRM measurement within T2. ​​If it is continuously satisfied within T2, the terminal evaluates whether the predicted event is continuously satisfied within T3 based on the prediction result of the RRM measurement prediction based on the AI ​​unit. If it is continuously satisfied within T3, the terminal triggers the measurement report. For example, for Event A3, if the neighboring cell measurement result is always higher than the serving cell measurement result by a preset threshold within the time to trigger time, then the A3 measurement event is satisfied; for the predicted Event A3, if the neighboring cell measurement result is always higher than the serving cell measurement result by a first threshold within T2, and the neighboring cell prediction result at the same time is always higher than the serving cell prediction result by a second threshold within T3, then the A3 prediction event is satisfied. As shown in Figure 3b, for the A3 measurement event, the entry condition is first met at time t0 and continues to be met during the time to trigger period, then the terminal triggers measurement reporting at time t0+T; for the A3 prediction event, the A3 prediction event first meets the entry condition at time t0, and the terminal determines based on the measurement result of the RRM measurement that the signal quality of the neighboring cell from t0 to t0+T2 is always higher than the signal quality of the serving cell by a first threshold, the terminal starts the RRM measurement prediction of the AI ​​unit at or before time t0+T2, and determines based on the prediction result that the signal quality of the neighboring cell from t0+T2 to t0+T3 is always higher than the signal quality of the serving cell by a second threshold, the terminal determines that the prediction event is met at time t0+T2, and triggers measurement reporting. Wherein, T=T2+T3, and the first threshold and the second threshold can be the same or different.

[0089] In the embodiments of the present application, the terminal can determine whether the prediction result obtained by performing RRM measurement prediction based on the AI ​​unit meets the prediction event based on the above two methods, thereby determining whether to trigger measurement reporting or conditional switching. Furthermore, in AI-assisted mobility enhancement, the terminal can perform measurement reporting or conditional switching earlier based on the predicted event, which helps to reduce switching latency and improve communication performance between the terminal and network-side equipment.

[0090] Optionally, the method further includes:

[0091] The terminal receives a first configuration sent by a network-side device, and determines the second time and the third time according to the first configuration.

[0092] That is to say, the second time and the third time are determined by the terminal according to the first configuration sent by the network side device. For example, the first configuration includes the second time and the third time, that is, the second time and the third time are directly configured by the network side device to the terminal, so that the terminal can judge whether the prediction result meets the prediction event based on the second time and the third time, which is helpful for the realization of terminal measurement reporting or conditional switching. For example, when the measurement result of the RRM measurement continues to meet the measurement event within the second time, and the prediction result of the RRM measurement prediction based on the AI ​​unit continues to meet the prediction event within the third time, the terminal determines that the prediction event is met, and then the terminal can trigger the measurement report or conditional switching to reduce the switching delay.

[0093] It should be noted that the first configuration may also be a reporting configuration sent by the network side device for RRM measurement.

[0094] Optionally, the first configuration includes any one of the following:

[0095] the second time and the third time;

[0096] the second time and the sum or difference between the second time and the third time;

[0097] The third time and the sum or difference between the third time and the second time.

[0098] Exemplarily, the first configuration includes the second time and the sum or difference between the second time and the third time, so that the terminal can calculate the third time. Alternatively, the first configuration includes the third time and the sum or difference between the second time and the third time, and the terminal can also calculate the second time. In an embodiment of the present application, the first configuration includes any of the above items, which can make the network-side device more flexible in configuring the second time and the third time.

[0099] Optionally, in the embodiment of the present application, before the terminal performs RRM measurement prediction based on the AI ​​unit, the method further includes:

[0100] The terminal receives a measurement configuration for RRM measurement sent by a network-side device, where at least one reporting configuration of the measurement configuration includes the predicted event.

[0101] It is understandable that the measurement configuration for RRM measurement generally includes a reporting configuration, a measurement object, and a measurement ID, so that the terminal can better perform RRM measurement and measurement reporting according to the measurement configuration.

[0102] In an embodiment of the present application, at least one reporting configuration of the measurement configuration includes the predicted event, that is, the network-side device can configure the terminal to perform RRM measurement prediction based on the AI ​​unit, and can configure what event is the event for which the RRM measurement prediction is performed, such as the prediction of the A1 event, the prediction of the A3 event, etc. Furthermore, by configuring the predicted event in at least one reporting configuration of the measurement configuration, the network-side device can better regulate the behavior of the terminal side, so that the terminal can perform RRM measurement prediction for the predicted event based on the AI ​​unit, obtain a prediction result, and trigger RRM measurement reporting or conditional switching when it is determined that the predicted event is met according to the prediction result.

[0103] Optionally, when the terminal determines that a predicted event is met according to the prediction result, triggering conditional switching includes any one of the following:

[0104] In a case where at least one reporting configuration of the measurement configuration includes both at least one measurement event and at least one prediction event, the terminal triggers conditional switching when determining, according to a measurement result of the RRM measurement, that at least one measurement event is satisfied and determining, according to the prediction result, that at least one prediction event is satisfied;

[0105] In a case where at least one reporting configuration of the measurement configuration includes at least one predicted event, the terminal triggers conditional switching when determining, according to the prediction result, that all predicted events are satisfied.

[0106] For example, in one implementation, if at least one reporting configuration of the measurement configuration includes at least one measurement event related to conditional switching and at least one prediction event related to conditional switching, the terminal evaluates whether the at least one measurement event is met based on the measurement result of the RRM measurement, and evaluates whether the at least one prediction event is met based on the prediction result of the RRM measurement prediction performed based on the AI ​​unit. When the measurement event and the prediction event are met at the same time, the terminal triggers conditional switching.

[0107] Alternatively, in another implementation, if at least one reporting configuration of the measurement configuration includes at least one predicted event related to conditional switching, the terminal evaluates whether the at least one predicted event is met based on the prediction result of the RRM measurement prediction performed based on the AI ​​unit, and when all predicted events are met, the terminal triggers conditional switching.

[0108] It should be noted that the measurement event being met means that the measurement result meets the entry condition or exit condition of the measurement event. For example, for an A3 event, if the neighboring cell measurement result is always higher than the serving cell measurement result by a preset threshold within time T1, the A3 measurement event is met. The specific implementation of the predicted event being met is as described above and will not be repeated here.

[0109] In the embodiment of the present application, the terminal can judge whether to trigger conditional switching based on the content configured in the reporting configuration, thereby better standardizing the behavior of executing conditional switching on the terminal side.

[0110] Optionally, when at least one reporting configuration of the measurement configuration includes a predicted event, the reporting configuration including the predicted event is associated with a first measurement identifier, and the first measurement identifier is a measurement identifier associated with an execution condition of the conditional switching.

[0111] It can be understood that the conditional switching includes a corresponding execution condition, and the execution condition of the conditional switching can be associated with a corresponding measurement identifier. For example, different execution conditions are associated with different measurement identifiers, which are used to distinguish the execution conditions and can be used to characterize the content of the execution condition. In the embodiment of the present application, the reporting configuration including the predicted event is associated with the measurement identifier (that is, the first measurement identifier) ​​associated with the execution condition of the conditional switching, which can further characterize that the predicted event included in the reporting configuration is related to the conditional switching. In this case, the first measurement identifier can instruct the terminal to decide whether to trigger the conditional switching based on the configured content of the reporting configuration, thereby further helping to standardize the behavior on the terminal side.

[0112] Optionally, the candidate cell configuration corresponding to the same conditional switching is associated with at least one first measurement identifier, and one first measurement identifier is associated with one predicted event. It can be understood that one first measurement identifier is associated with one predicted event, and the candidate cell corresponding to the same conditional switching can be configured to be associated with multiple first measurement identifiers, that is, associated with multiple predicted events. In this case, the terminal triggers the conditional switching only when it determines that all predicted events are met based on the prediction result obtained by the RRM measurement prediction based on the AI ​​unit.

[0113] Optionally, the reporting configuration or the measurement identifier configuration sent by the network side device further includes a first identifier, where the first identifier is an identifier of the AI ​​unit or an AI function identifier.

[0114] Optionally, in this case, the terminal performs RRM measurement prediction based on the AI ​​unit to obtain a prediction result, including:

[0115] The terminal performs RRM measurement prediction according to the AI ​​unit corresponding to the first identifier to obtain a prediction result.

[0116] In an embodiment of the present application, the network-side device carries a first identifier in the reporting configuration or measurement identifier configuration, where the first identifier is an identifier of an AI unit or an AI function identifier, and the terminal can then use the AI ​​unit corresponding to the first identifier to perform RRM measurement prediction. In this way, the network-side device can configure the terminal to use the corresponding AI unit for RRM measurement prediction based on the first identifier, so that both the network-side device and the terminal can clearly identify which AI unit is used for RRM measurement prediction, which facilitates information synchronization between the network-side device and the terminal.

[0117] Optionally, in an embodiment of the present application, the method further includes:

[0118] The terminal receives a conditional reconfiguration for conditional switching sent by a network side device;

[0119] When the terminal determines, based on the prediction result, that a prediction event is satisfied, triggering conditional switching includes:

[0120] The terminal is reconfigured according to the condition, and triggers conditional switching when it is determined according to the prediction result that a prediction event is met.

[0121] It is understandable that when the network-side device sends a conditional reconfiguration for conditional switching to the terminal, the terminal needs to determine whether to trigger conditional switching based on the conditional reconfiguration and whether the prediction result obtained by the RRM measurement prediction based on the AI ​​unit meets the prediction event. The relevant parameters of the conditional reconfiguration can refer to the relevant technology.

[0122] Optionally, in the embodiment of the present application, after triggering the RRM measurement reporting or conditional switching, the method further includes:

[0123] The terminal reports a first measurement report, where the first measurement report includes at least one of the following:

[0124] the predicted result;

[0125] Measurement results of RRM measurements.

[0126] It is understandable that during the communication process between the terminal and the network side device, the terminal needs to perform RRM measurement and can report the measurement result of the RRM measurement. In an embodiment of the present application, the terminal can perform RRM measurement prediction based on the AI ​​unit to obtain a prediction result. The terminal can report the prediction result together with the measurement result of the RRM measurement or report it separately, or it can only report the prediction result, or only report the measurement result of the RRM measurement, thereby making the reporting behavior on the terminal side more flexible.

[0127] Please refer to Figure 4, which is a flowchart of another RRM measurement processing method provided by an embodiment of the present application, the method is applied to a network side device. As shown in Figure 4, the method includes the following steps:

[0128] Step 401: The network-side device sends a measurement configuration for RRM measurement to the terminal, where at least one reporting configuration of the measurement configuration includes a predicted event;

[0129] The predicted event is an event predicted by the terminal based on the AI ​​unit, and the terminal is configured to trigger RRM measurement reporting or conditional switching when a prediction result obtained by performing RRM measurement prediction based on the AI ​​unit meets the predicted event.

[0130] Optionally, the reporting configuration including the predicted event is associated with a first measurement identifier, where the first measurement identifier is a measurement identifier associated with an execution condition of the conditional switching.

[0131] Optionally, the candidate cell configuration corresponding to the same conditional switching is associated with at least one first measurement identifier, and one first measurement identifier is associated with one predicted event.

[0132] Optionally, the reporting configuration or the measurement identifier configuration sent by the network-side device further includes a first identifier, where the first identifier is an identifier of the AI ​​unit or an AI function identifier.

[0133] Optionally, the method further includes:

[0134] The network side device sends a first configuration to the terminal, where the first configuration is used by the terminal to determine a second time and a third time, where the second time is used by the terminal to determine whether a measurement event is met according to a measurement result of an RRM measurement, and the third time is used by the terminal to determine whether the predicted event is met according to a predicted result.

[0135] Optionally, the first configuration includes any one of the following:

[0136] the second time and the third time;

[0137] the second time and the sum or difference between the second time and the third time;

[0138] The third time and the sum or difference between the third time and the second time.

[0139] Optionally, the method further includes:

[0140] The network-side device receives a first measurement result reported by the terminal, where the first measurement report includes at least one of the following:

[0141] the predicted result;

[0142] Measurement results of RRM measurements.

[0143] Optionally, the method further includes:

[0144] The network side device sends a conditional reconfiguration for conditional switching to the terminal.

[0145] It should be noted that the method provided in the embodiment of the present application corresponds to the above-mentioned method applied to the terminal side. The relevant concepts and specific implementation processes involved in the embodiment of the present application can be referred to the description in the embodiment of the terminal side method, and will not be repeated in this embodiment.

[0146] In an embodiment of the present application, a network-side device sends a measurement configuration for RRM measurement to a terminal, and at least one reporting configuration of the measurement configuration includes a prediction event, where the prediction event is an event predicted by the terminal based on an AI unit. The terminal is configured to trigger an RRM measurement report or conditional switching when a prediction result obtained by performing RRM measurement prediction based on the AI ​​unit meets the prediction event, which helps to reduce the switching delay of the terminal. It also specifies how the terminal triggers measurement reporting or conditional switching based on the prediction result of the AI-based RRM measurement prediction in AI-assisted mobility enhancement, effectively regulating the behavior of the terminal side.

[0147] The RRM measurement processing method provided in the embodiment of the present application may be executed by an RRM measurement processing device. In the embodiment of the present application, the RRM measurement processing device performing the RRM measurement processing method is taken as an example to illustrate the RRM measurement processing device provided in the embodiment of the present application.

[0148] Please refer to Figure 5, which is a structural diagram of an RRM measurement processing device provided in an embodiment of the present application. As shown in Figure 5, the RRM measurement processing device 500 includes:

[0149] A prediction module 501 is configured to perform RRM measurement prediction based on the AI ​​unit to obtain a prediction result;

[0150] A triggering module 502 is configured to trigger RRM measurement reporting or conditional switching when it is determined that a prediction event is met according to the prediction result;

[0151] The predicted event is an event predicted by the device based on the AI ​​unit.

[0152] Optionally, the trigger module 502 is further configured to:

[0153] If the prediction result continuously satisfies the prediction event within the first time, determining that the prediction event is satisfied;

[0154] If the measurement result of the RRM measurement continuously satisfies the measurement event within the second time, and the prediction result continuously satisfies the prediction event within the third time, it is determined that the prediction event is satisfied.

[0155] Optionally, the device further comprises:

[0156] The first receiving module is configured to receive a first configuration sent by a network-side device, and determine the second time and the third time according to the first configuration.

[0157] Optionally, the first configuration includes any one of the following:

[0158] the second time and the third time;

[0159] the second time and the sum or difference between the second time and the third time;

[0160] The third time and the sum or difference between the third time and the second time.

[0161] Optionally, the device further comprises:

[0162] The second receiving module is configured to receive a measurement configuration for RRM measurement sent by a network-side device, wherein at least one reporting configuration of the measurement configuration includes the predicted event.

[0163] Optionally, the trigger module 502 is further configured to:

[0164] In a case where at least one reporting configuration of the measurement configuration includes both at least one measurement event and at least one prediction event, triggering conditional switching when determining, according to a measurement result of the RRM measurement, that at least one measurement event is satisfied, and determining, according to the prediction result, that at least one prediction event is satisfied;

[0165] In a case where at least one reporting configuration of the measurement configuration includes at least one predicted event, conditional switching is triggered when it is determined according to the prediction result that all predicted events are satisfied.

[0166] Optionally, the reporting configuration including the predicted event is associated with a first measurement identifier, where the first measurement identifier is a measurement identifier associated with an execution condition of the conditional switching.

[0167] Optionally, the candidate cell configuration corresponding to the same conditional switching is associated with at least one first measurement identifier, and one first measurement identifier is associated with one predicted event.

[0168] Optionally, the reporting configuration or the measurement identifier configuration sent by the network side device further includes a first identifier, where the first identifier is an identifier of the AI ​​unit or an AI function identifier.

[0169] Optionally, the prediction module 501 is further configured to:

[0170] Perform RRM measurement prediction according to the AI ​​unit corresponding to the first identifier to obtain a prediction result.

[0171] Optionally, the device further comprises:

[0172] A reporting module, configured to report a first measurement report, where the first measurement report includes at least one of the following:

[0173] the predicted result;

[0174] Measurement results of RRM measurements.

[0175] Optionally, the device further comprises:

[0176] A third receiving module, configured to receive a conditional reconfiguration for conditional switching sent by a network-side device;

[0177] The trigger module 502 is further configured to:

[0178] Reconfigure according to the condition, and trigger conditional switching when it is determined according to the prediction result that the prediction event is met.

[0179] In an embodiment of the present application, the device performs RRM measurement prediction based on the AI ​​unit to obtain a prediction result. When it is determined that the prediction event is met according to the prediction result, the RRM measurement reporting or conditional switching is triggered, thereby helping to reduce the switching delay. It also stipulates how to trigger measurement reporting or conditional switching according to the prediction result of the AI-based RRM measurement prediction in AI-assisted mobility enhancement.

[0180] The RRM measurement processing device in the embodiment of the present application can be an electronic device, such as an electronic device having an operating system, or a component in an electronic device, such as an integrated circuit or chip. The electronic device can be a terminal, or it can be a device other than a terminal. For example, the terminal can include but is not limited to the types of terminal 11 listed above, and the other device can be a server, a network attached storage (NAS), etc., which is not specifically limited in the embodiment of the present application.

[0181] The RRM measurement processing device provided in the embodiment of the present application can implement each process implemented in the method embodiment of Figure 2 and achieve the same technical effect. To avoid repetition, it will not be described here.

[0182] Please refer to FIG6 , which is a structural diagram of another RRM measurement processing device provided in an embodiment of the present application. As shown in FIG6 , the RRM measurement processing device 600 includes:

[0183] A sending module 601 is configured to send a measurement configuration for RRM measurement to a terminal, where at least one reporting configuration of the measurement configuration includes a prediction event;

[0184] The predicted event is an event predicted by the terminal based on the AI ​​unit, and the terminal is configured to trigger RRM measurement reporting or conditional switching when a prediction result obtained by performing RRM measurement prediction based on the AI ​​unit meets the predicted event.

[0185] Optionally, the reporting configuration including the predicted event is associated with a first measurement identifier, where the first measurement identifier is a measurement identifier associated with an execution condition of the conditional switching.

[0186] Optionally, the candidate cell configuration corresponding to the same conditional switching is associated with at least one first measurement identifier, and one first measurement identifier is associated with one predicted event.

[0187] Optionally, the reporting configuration or the measurement identifier configuration sent by the network side device further includes a first identifier, where the first identifier is an identifier of the AI ​​unit or an AI function identifier.

[0188] Optionally, the sending module 601 is further configured to:

[0189] A first configuration is sent to the terminal, where the first configuration is used by the terminal to determine a second time and a third time, where the second time is used by the terminal to determine whether a measurement event is met according to a measurement result of an RRM measurement, and the third time is used by the terminal to determine whether the predicted event is met according to the predicted result.

[0190] Optionally, the first configuration includes any one of the following:

[0191] the second time and the third time;

[0192] the second time and the sum or difference between the second time and the third time;

[0193] The third time and the sum or difference between the third time and the second time.

[0194] Optionally, the device further comprises:

[0195] A receiving module, configured to receive a first measurement result reported by a terminal, where the first measurement report includes at least one of the following:

[0196] the predicted result;

[0197] Measurement results of RRM measurements.

[0198] Optionally, the sending module 601 is further configured to:

[0199] Sending conditional reconfiguration for conditional switching to the terminal.

[0200] The RRM measurement processing device provided in the embodiment of the present application can implement each process implemented in the method embodiment of Figure 4 and achieve the same technical effect. To avoid repetition, it will not be described here.

[0201] As shown in Figure 7, an embodiment of the present application further provides a communication device 700, including a processor 701 and a memory 702. The memory 702 stores a program or instruction that can be executed on the processor 701. For example, when the communication device 700 is a terminal, the program or instruction, when executed by the processor 701, implements the various steps of the above-mentioned RRM measurement processing method embodiment and can achieve the same technical effect. When the communication device 700 is a network-side device, the program or instruction, when executed by the processor 701, implements the various steps of the above-mentioned RRM measurement processing method embodiment and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0202] The present application also provides a terminal comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is configured to execute a program or instruction to implement the steps of the method embodiment shown in FIG2 . This terminal embodiment corresponds to the aforementioned terminal-side method embodiment, and each implementation process and implementation method of the aforementioned method embodiment is applicable to this terminal embodiment and can achieve the same technical effects. Specifically, FIG8 is a schematic diagram of the hardware structure of a terminal implementing an embodiment of the present application.

[0203] The terminal 800 includes but is not limited to: a radio frequency unit 801, a network module 802, an audio output unit 803, an input unit 804, a sensor 805, a display unit 806, a user input unit 807, an interface unit 808, a memory 809 and at least some of the components of the processor 810.

[0204] Those skilled in the art will appreciate that the terminal 800 may also include a power supply (such as a battery) to power various components. The power supply may be logically connected to the processor 810 via a power management system, thereby enabling the power management system to manage charging, discharging, and power consumption. The terminal structure shown in FIG8 does not limit the terminal. The terminal may include more or fewer components than shown, or may combine certain components, or have different component arrangements, which will not be described in detail here.

[0205] It should be understood that in an embodiment of the present application, the input unit 804 may include a graphics processing unit (GPU) 8041 and a microphone 8042, and the graphics processor 8041 processes the image data of a static picture or video obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode. The display unit 806 may include a display panel 8061, and the display panel 8061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc. The user input unit 807 includes a touch panel 8071 and at least one of other input devices 8072. The touch panel 8071 is also called a touch screen. The touch panel 8071 may include two parts: a touch detection device and a touch controller. Other input devices 8072 may include, but are not limited to, a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which will not be repeated here.

[0206] In the embodiment of the present application, after receiving downlink data from a network-side device, the radio frequency unit 801 may transmit the data to the processor 810 for processing. Furthermore, the radio frequency unit 801 may send uplink data to the network-side device. Typically, the radio frequency unit 801 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low-noise amplifier, a duplexer, and the like.

[0207] The memory 809 can be used to store software programs or instructions and various data. The memory 809 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, applications or instructions required for at least one function (such as a sound playback function, an image playback function, etc.). In addition, the memory 809 may include a volatile memory or a non-volatile memory. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct RAM bus random access memory (DRRAM). The memory 809 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

[0208] Processor 810 may include one or more processing units. Optionally, processor 810 integrates an application processor and a modem processor. The application processor primarily handles operations related to the operating system, user interface, and application programs, while the modem processor primarily processes wireless communication signals, such as a baseband processor. It is understood that the modem processor may not be integrated into processor 810.

[0209] The processor 810 is configured to:

[0210] Perform RRM measurement prediction based on the AI ​​unit to obtain prediction results;

[0211] When it is determined according to the prediction result that the prediction event is met, triggering RRM measurement reporting or conditional switching;

[0212] The predicted event is an event predicted by the device based on the AI ​​unit.

[0213] In an embodiment of the present application, the terminal performs RRM measurement prediction based on the AI ​​unit and obtains a prediction result. When it is determined that the prediction event is met according to the prediction result, the terminal triggers RRM measurement reporting or conditional switching, which helps to reduce the switching delay. It also stipulates how the terminal triggers measurement reporting or conditional switching according to the prediction result of the AI-based RRM measurement prediction in AI-assisted mobility enhancement, effectively standardizes the behavior on the terminal side, and helps to improve the communication performance between the terminal and the network side device.

[0214] It can be understood that the implementation process of each implementation method mentioned in this embodiment can refer to the relevant description of the above-mentioned RRM measurement processing method embodiment and achieve the same or corresponding technical effects. To avoid repetition, it will not be repeated here.

[0215] The present application also provides a network-side device, including a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is configured to execute a program or instruction to implement the steps of the method embodiment shown in FIG4 . This network-side device embodiment corresponds to the aforementioned network-side device method embodiment, and each implementation process and implementation method of the aforementioned method embodiment are applicable to this network-side device embodiment and can achieve the same technical effects.

[0216] Specifically, embodiments of the present application also provide a network-side device. As shown in Figure 9, the network-side device 900 includes an antenna 91, a radio frequency device 92, a baseband device 93, a processor 94, and a memory 95. Antenna 91 is connected to radio frequency device 92. In the uplink direction, radio frequency device 92 receives information via antenna 91 and sends the received information to baseband device 93 for processing. In the downlink direction, baseband device 93 processes the information to be transmitted and sends it to radio frequency device 92. Radio frequency device 92 processes the received information and then sends it through antenna 91.

[0217] The method executed by the network-side device in the above embodiment may be implemented in the baseband device 93 , which includes a baseband processor.

[0218] The baseband device 93 may include, for example, at least one baseband board, on which multiple chips are arranged, as shown in Figure 9, one of the chips is, for example, a baseband processor, which is connected to the memory 95 through a bus interface to call the program in the memory 95 and execute the network side device operations shown in the above method embodiment.

[0219] The network side device may further include a network interface 96, which is, for example, a Common Public Radio Interface (CPRI).

[0220] Specifically, the network side device 900 of the embodiment of the present application also includes: instructions or programs stored in the memory 95 and executable on the processor 94. The processor 94 calls the instructions or programs in the memory 95 to execute the methods of execution of each module shown in FIG6 and achieve the same technical effect. To avoid repetition, it will not be described here.

[0221] An embodiment of the present application further provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, each process of the above-mentioned RRM measurement processing method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it is not repeated here.

[0222] The processor is the processor in the terminal described in the above embodiment. The readable storage medium includes a computer-readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk. In some examples, the readable storage medium may be a non-transitory readable storage medium.

[0223] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, the communication interface and the processor are coupled, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned RRM measurement processing method embodiment, and can achieve the same technical effect. To avoid repetition, it is not repeated here.

[0224] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0225] An embodiment of the present application further provides a computer program / program product, which is stored in a storage medium. The computer program / program product is executed by at least one processor to implement the various processes of the above-mentioned RRM measurement processing method embodiment, and can achieve the same technical effects. To avoid repetition, it is not repeated here.

[0226] An embodiment of the present application further provides a communication system, including: a terminal and a network-side device, wherein the terminal can be used to execute the steps of the RRM measurement processing method described above, and the network-side device can be used to execute the steps of the RRM measurement processing method described above.

[0227] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the opposite order according to the functions involved. For example, the described method may be performed in an order different from that described, and various steps may also be added, omitted or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0228] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of a computer software product plus a necessary general-purpose hardware platform, or of course, by hardware. The computer software product is stored in a storage medium (such as ROM, RAM, magnetic disk, optical disk, etc.) and includes a number of instructions for enabling a terminal or network-side device to execute the methods described in each embodiment of the present application.

[0229] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms of implementation methods without departing from the purpose of this application and the scope of protection of the claims. These implementation methods are all within the protection of this application.

Claims

1. A method for processing radio resource management (RRM) measurements, comprising: The terminal performs RRM measurement prediction based on an artificial intelligence (AI) unit to obtain a prediction result; When the terminal determines that a prediction event is satisfied according to the prediction result, the terminal triggers RRM measurement reporting or conditional handover; Wherein, the prediction event is an event predicted by the terminal based on the AI unit.

2. The method according to claim 1, wherein The terminal determines that the prediction event is satisfied according to the prediction result, including any one of the following: When the prediction result continuously satisfies the prediction event within a first time period, the terminal determines that the prediction event is satisfied; When the measurement result of the RRM measurement continuously satisfies a measurement event within a second time period, and the prediction result continuously satisfies the prediction event within a third time period, the terminal determines that the prediction event is satisfied.

3. The method according to claim 2, wherein The method further includes: The terminal receives a first configuration sent by a network-side device and determines the second time period and the third time period according to the first configuration.

4. The method according to claim 3, wherein The first configuration includes any one of the following: The second time period and the third time period; The second time period, and the sum or difference between the second time period and the third time period; The third time period, and the sum or difference between the third time period and the second time period.

5. The method according to any one of claims 1-4, wherein Before the terminal performs RRM measurement prediction based on the AI unit, the method further includes: The terminal receives a measurement configuration for RRM measurement sent by a network-side device, and at least one reporting configuration of the measurement configuration includes the prediction event.

6. The method according to claim 5, wherein, When the terminal determines that a prediction event is satisfied according to the prediction result and triggers conditional handover, it includes any one of the following: When at least one reporting configuration of the measurement configuration includes at least one measurement event and at least one prediction event, the terminal triggers conditional handover when the measurement result of the RRM measurement determines that at least one measurement event is satisfied and the prediction result determines that at least one prediction event is satisfied; When at least one reporting configuration of the measurement configuration includes at least one prediction event, the terminal triggers conditional handover when the prediction result determines that all prediction events are satisfied.

7. The method according to claim 6, wherein, The reporting configuration including the prediction event is associated with a first measurement identifier, and the first measurement identifier is a measurement identifier associated with the execution condition of the conditional handover.

8. The method according to claim 7, wherein, For the same conditional handover, the candidate cell configuration is associated with at least one of the first measurement identifiers, and one first measurement identifier is associated with one prediction event.

9. The method according to claim 5, wherein The reporting configuration or the measurement identifier configuration sent by the network-side device further includes a first identifier, and the first identifier is an identifier of the AI unit or an AI function identifier.

10. The method according to claim 9, wherein, The terminal performs RRM measurement prediction based on the AI unit to obtain a prediction result, including: The terminal performs RRM measurement prediction according to the AI unit corresponding to the first identifier to obtain a prediction result.

11. According to the method according to any one of claims 1-10, wherein, After triggering RRM measurement reporting or conditional handover, the method further includes: The terminal reports a first measurement report, and the first measurement report includes at least one of the following: The prediction result; The measurement result of the RRM measurement.

12. The method according to any one of claims 1-10, wherein, The method further includes: The terminal receives a conditional reconfiguration for conditional handover sent by a network-side device; When the terminal determines that a prediction event is satisfied according to the prediction result, the terminal triggers a conditional handover, including: The terminal triggers a conditional handover according to the conditional reconfiguration and when it determines that a prediction event is satisfied according to the prediction result.

13. An RRM measurement processing method, including: A network-side device sends a measurement configuration for RRM measurement to a terminal, and at least one reporting configuration of the measurement configuration includes a prediction event; Wherein, the prediction event is an event predicted by the terminal based on an AI unit, and the terminal is used to trigger an RRM measurement report or a conditional handover when a prediction result obtained by predicting an RRM measurement based on the AI unit satisfies the prediction event.

14. The method according to claim 13, wherein, The reporting configuration including the prediction event is associated with a first measurement identifier, and the first measurement identifier is a measurement identifier associated with an execution condition of the conditional handover.

15. The method according to claim 14, wherein At least one of the first measurement identifiers is associated with a candidate cell configuration corresponding to the same conditional handover, and one first measurement identifier is associated with one prediction event.

16. The method according to claim 13, wherein, The reporting configuration or a measurement identifier configuration sent by the network-side device further includes a first identifier, and the first identifier is an identifier of the AI unit or an AI function identifier.

17. The method according to claim 13, wherein The method further includes: The network-side device sends a first configuration to the terminal, and the first configuration is used for the terminal to determine a second time and a third time. The second time is used for the terminal to determine whether a measurement event is satisfied according to a measurement result of an RRM measurement, and the third time is used for the terminal to determine whether the prediction event is satisfied according to the predicted result.

18. The method according to claim 17, wherein, The first configuration includes any one of the following: The second time and the third time; The second time and the sum or difference between the second time and the third time; The third time and the sum or difference between the third time and the second time.

19. The method according to any one of claims 13 - 18, wherein, The method further includes: The network-side device receives a first measurement result reported by the terminal, and the first measurement report includes at least one of the following: The prediction result; A measurement result of an RRM measurement.

20. The method according to any one of claims 13-18, wherein The method further includes: The network-side device sends a conditional reconfiguration for conditional handover to the terminal.

21. An RRM measurement processing apparatus, including: A prediction module, configured to perform RRM measurement prediction based on an AI unit to obtain a prediction result; A trigger module, configured to trigger an RRM measurement report or a conditional handover when it determines that a prediction event is satisfied according to the prediction result; Wherein, the prediction event is an event predicted by the apparatus based on an AI unit.

22. The device according to claim 21, wherein, The trigger module is further configured to perform any one of the following: When the prediction result continuously satisfies the prediction event within a first time, determine that the prediction event is satisfied; When a measurement result of an RRM measurement continuously satisfies a measurement event within a second time and the prediction result continuously satisfies the prediction event within a third time, determine that the prediction event is satisfied.

23. The device according to claim 22, wherein, The apparatus further includes: A first receiving module, configured to receive a first configuration sent by a network-side device, and determine the second time and the third time according to the first configuration.

24. The device according to any one of claims 21-23, wherein, The apparatus further includes: A second receiving module, configured to receive a measurement configuration for RRM measurement sent by a network-side device, where at least one reporting configuration of the measurement configuration includes the prediction event.

25. The apparatus according to claim 24, wherein, The triggering module is further configured to perform any one of the following: When at least one measurement event and at least one prediction event are included in at least one reporting configuration of the measurement configuration, triggering a condition switch when it is determined according to the measurement result of the RRM measurement that at least one measurement event is satisfied and it is determined according to the prediction result that at least one prediction event is satisfied; When at least one prediction event is included in at least one reporting configuration of the measurement configuration, triggering a condition switch when it is determined according to the prediction result that all prediction events are satisfied.

26. An RRM measurement processing apparatus, including: A sending module, configured to send a measurement configuration for RRM measurement to a terminal, where at least one reporting configuration of the measurement configuration includes a prediction event; Wherein, the prediction event is an event predicted by the terminal based on an AI unit, and the terminal is configured to trigger an RRM measurement report or a condition switch when the prediction result obtained by predicting the RRM measurement according to the AI unit satisfies the prediction event.

27. The apparatus according to claim 26, wherein, The sending module is further configured to: Send a first configuration to the terminal, where the first configuration is used for the terminal to determine a second time and a third time, the second time is used for the terminal to determine whether a measurement event is satisfied according to the measurement result of the RRM measurement, and the third time is used for the terminal to determine whether the prediction event is satisfied according to the prediction result.

28. The device according to claim 26 or 27, wherein, The apparatus further includes: A receiving module, configured to receive a first measurement result reported by the terminal, where the first measurement report includes at least one of the following: The prediction result; The measurement result of the RRM measurement.

29. A terminal, including a processor and a memory, where the memory stores a program or instruction that can run on the processor, and when the program or instruction is executed by the processor, the steps of the RRM measurement processing method according to any one of claims 1-12 are implemented.

30. A network-side device, including a processor and a memory, where the memory stores a program or instruction that can run on the processor, and when the program or instruction is executed by the processor, the steps of the RRM measurement processing method according to any one of claims 13-20 are implemented.

31. A readable storage medium, wherein, A program or instruction is stored on the readable storage medium, and when the program or instruction is executed by a processor, the steps of the RRM measurement processing method according to any one of claims 1-12 are implemented, or the steps of the RRM measurement processing method according to any one of claims 13-20 are implemented.

32. A computer program product, wherein, The program product is executed by at least one processor to implement the steps of the RRM measurement processing method according to any one of claims 1-12, or to implement the steps of the RRM measurement processing method according to any one of claims 13-20.

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